from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match¶reporting = Reporting("sklearnex", config="config.yml")
reporting.run()
KNeighborsClassifier_brute_force: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100 | 100 | 10 | NaN | 0.0 | 0.0 | 0.022 | 0.0 | -1 | 100 | NaN | NaN | 0.001 | 0.0 | 0.271 | 0.0 | See | See |
| 2 | KNeighborsClassifier_brute_force | fit | 100 | 100 | 10 | NaN | 0.0 | 0.0 | 0.030 | 0.0 | 1 | 100 | NaN | NaN | 0.000 | 0.0 | 0.612 | 0.0 | See | See |
| 4 | KNeighborsClassifier_brute_force | fit | 100 | 100 | 10 | NaN | 0.0 | 0.0 | 0.029 | 0.0 | -1 | 1 | NaN | NaN | 0.000 | 0.0 | 0.648 | 0.0 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100 | 100 | 10 | NaN | 0.0 | 0.0 | 0.030 | 0.0 | 1 | 5 | NaN | NaN | 0.000 | 0.0 | 0.624 | 0.0 | See | See |
| 8 | KNeighborsClassifier_brute_force | fit | 100 | 100 | 10 | NaN | 0.0 | 0.0 | 0.029 | 0.0 | 1 | 1 | NaN | NaN | 0.000 | 0.0 | 0.620 | 0.0 | See | See |
| 10 | KNeighborsClassifier_brute_force | fit | 100 | 100 | 10 | NaN | 0.0 | 0.0 | 0.028 | 0.0 | -1 | 5 | NaN | NaN | 0.000 | 0.0 | 0.662 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100 | 1 | 10 | NaN | 0.004 | 0.001 | 0.0 | 0.004 | -1 | 100 | 0.0 | 1.0 | 0.0 | 0.0 | 26.483 | 17.440 | See | See |
| 3 | KNeighborsClassifier_brute_force | predict | 100 | 1 | 10 | NaN | 0.001 | 0.000 | 0.0 | 0.001 | 1 | 100 | 0.0 | 1.0 | 0.0 | 0.0 | 3.622 | 2.196 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100 | 1 | 10 | NaN | 0.003 | 0.000 | 0.0 | 0.003 | -1 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 24.394 | 16.597 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100 | 1 | 10 | NaN | 0.001 | 0.000 | 0.0 | 0.001 | 1 | 5 | 1.0 | 0.0 | 0.0 | 0.0 | 4.165 | 2.966 | See | See |
| 9 | KNeighborsClassifier_brute_force | predict | 100 | 1 | 10 | NaN | 0.001 | 0.000 | 0.0 | 0.001 | 1 | 1 | 1.0 | 1.0 | 0.0 | 0.0 | 4.078 | 2.756 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100 | 1 | 10 | NaN | 0.004 | 0.001 | 0.0 | 0.004 | -1 | 5 | 1.0 | 1.0 | 0.0 | 0.0 | 22.282 | 13.428 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 100 | 100 | 10 | 16 | 0.045 | 0.0 | 0.003 | 0.0 | k-means++ | NaN | 15 | NaN | 0.001 | 0.0 | 35.797 | 0.0 | See | See |
| 2 | KMeans_tall | fit | 100 | 100 | 10 | 7 | 0.004 | 0.0 | 0.014 | 0.0 | random | NaN | 8 | NaN | 0.001 | 0.0 | 4.518 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 100 | 1 | 10 | 16 | 0.002 | 0.0 | 0.001 | 0.002 | k-means++ | 1.0 | 15 | 1.0 | 0.0 | 0.0 | 11.333 | 6.713 | See | See |
| 3 | KMeans_tall | predict | 100 | 1 | 10 | 7 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.0 | 8 | 1.0 | 0.0 | 0.0 | 11.421 | 6.753 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=50, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 100 | 100 | 10 | 3 | 0.004 | 0.0 | 0.006 | 0.0 | random | NaN | 4 | NaN | 0.001 | 0.0 | 2.915 | 0.0 | See | See |
| 2 | KMeans_short | fit | 100 | 100 | 10 | 3 | 0.012 | 0.0 | 0.002 | 0.0 | k-means++ | NaN | 3 | NaN | 0.001 | 0.0 | 13.002 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 100 | 1 | 10 | 3 | 0.002 | 0.0 | 0.0 | 0.002 | random | 1.0 | 4 | 1.0 | 0.0 | 0.0 | 10.531 | 5.830 | See | See |
| 3 | KMeans_short | predict | 100 | 1 | 10 | 3 | 0.002 | 0.0 | 0.0 | 0.002 | k-means++ | 1.0 | 3 | 1.0 | 0.0 | 0.0 | 10.950 | 6.738 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 100 | 100 | 10 | [11] | 0.003 | 0.0 | [0.02727495] | 0.0 | NaN | NaN | NaN | NaN | NaN | 0.002 | 0.0 | 1.319 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 100 | 1 | 10 | [11] | 0.0 | 0.0 | [0.01167073] | 0.0 | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 0.0 | 0.407 | 0.361 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 100 | 100 | 10 | NaN | 0.001 | 0.0 | 0.01 | 0.0 | NaN | NaN | NaN | 0.001 | 0.0 | 1.262 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 100 | 1 | 10 | NaN | 0.0 | 0.0 | 0.001 | 0.0 | NaN | NaN | NaN | 0.0 | 0.0 | 0.597 | 0.607 | See | See |